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    Statistical Analyses of the Perceptions and Trust of Adaptive Cruise Control Owners

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    Wu_washington_0250O_10109.pdf (1.886Mb)
    Date
    2012-08-10
    Author
    Wu, Yuqing
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    Abstract
    Adaptive Cruise Control (ACC), an advanced version of conventional cruise control (CCC), can detect the distance to a vehicle ahead and assist drivers in maintaining a set speed and headway distance. The system has been available in the US since 2001, and there are approximately 70 vehicle models with ACC as standard or optional feature on the road. This study examined the characteristics of drivers that own ACC including their perceptions toward the system, and willingness to trust the system to take control in various situations. Survey data on ACC preferences from Washington State were used to examine both issues. A binary logistic model was used to examine the likelihood that a driver would own a vehicle with ACC. The findings showed that younger (< 45 years old) drivers were more likely to be ACC owners. Further, the type of vehicle that respondents stated they own also influenced ACC ownership. More specifically, Toyota or Lexus owners were more likely to be ACC owners when compared to other vehicle brands. The findings also suggested that those who selected their vehicle because it was perceived to be safe were less likely to own a vehicle with ACC. ACC owners reported higher levels of trust in ACC but this finding might be biased given their existing experience with the system. An ordered logistic model with only ACC owners was then conducted to explore the issue of trust further. Higher trust was associated with drivers' perceptions of ACC (safety and convenient issues) and driver behavior (ACC usage). An exploratory word cloud analysis was conducted to obtain additional insights on drivers' safety concerns with the system. Of the 34 that responded, safety concerns related to ACC's braking capability and gap settings were raised, which can be explored in future studies.
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    http://hdl.handle.net/1773/20218
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    • Industrial engineering [60]

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